Forward deployed AI
Every company can buy AI. Few can deploy it.
For teams that have the models, and not the deployment.
AI can generate answers. It can't decide where it belongs.
Pilots die between the demo and the workflow.
No handoff.
A design sprint that audits the real workflow, then agents, evals and a staged rollout on top of the systems you already run.
One operator. Thirty days. Deployed.
This is what “thirty days” actually looks like.
WITHOUT A SPRINT
- Week 1 — AI strategy kickoff. Everyone is excited.
- Week 2 — Vendor demos. Nobody has touched real data.
- Week 4 — A chatbot over the PDFs. Nobody uses it.
- Week 7 — Security review of a tool nobody chose.
- Week 11 — Pilot plan, third revision.
- Week 16 — IT says it can't touch the ERP.
- Week 24 — New vendor. Start again.
Six months in. Still nobody has clicked anything.
WITH A SPRINT
- Day 1 — Trace the real workflow — every email, sheet and exception.
- Day 3 — One use case, its boundaries, and what it's worth.
- Day 5 — The approval screen, in code, on a live link.
- Weeks 2–4 — Agent, evals, and a shadow run on your systems.
- Shipped in week 4.
Everything past here is iteration on software real people are already using.
The thirty days, unpacked
Audit. Build. Evals. Deploy.
The timeline above is the why. This is the what — each stage earns the right to the next.
- Week 1 · Design sprint
Audit the workflow that's actually running.
Interviews, emails, spreadsheets, SOPs and every exception path, traced into one operating map: how the work happens today, how it should run with AI in it, the one use case worth doing first, and what it's worth in hours, cost and errors.
- Week 2
Build it on top of what you already run.
Rules go in code, judgment goes to an agent, and one clear decision goes to a person. Every prompt, tool call and approval is logged. It lives in your stack and your repo, not a vendor's.
- Week 3
Turn "it works" into evidence.
Evaluation cases for the normal path, the edge case, missing data, ambiguous requests and high-risk actions. You get a pass rate, the failure categories, and the rules for when it must escalate to a human.
- Week 4
Deploy in shadow, then earn autonomy.
It runs alongside your team inside your own infrastructure, with logs, alerts and rollback. A go/no-go readout for engineering and leadership, then a 14-day support window.
Free · 20 minutes · nothing to prepare
Find the workflow worth rebuilding with AI
Walk me through how the work actually gets done today — the emails, spreadsheets, approvals and exceptions. I'll show you where AI belongs, where it doesn't, and what I'd build first.
You'll leave with the one workflow to start on and a rough sense of what it's worth.
Book your free workflow reviewNo deck. No prep. No sales pitch.
See how I think before you book
Watch me tear a real product apart.
Before you trust me with your workflow, ride along on a review I've already done — a real 24-minute session with a product you know, narrated start to finish.
PerplexitySample review7 min experienceThe Invisible Meter
Why the paywall isn't what lost them.
- A real session, narrated beat by beat in my own recorded voice
- The intent curve — the exact moment motivation broke, and why
- A severity scoreboard, ordered the way you'd actually schedule the fixes
- Three changes you could ship Monday, each indefensible once it's named
Yours is free, live, and about your workflow — not a stranger's product.
Selected work trusted by teams that real people use today.
Three ways to work together
All three start the same way: 20 free minutes on a call, going through the workflow you'd hand to AI first. No decks, no prep, no runaround.
Fixed price · 1 week
Design Sprint
The audit. Find the one workflow worth rebuilding with AI, and decide exactly what the system may and may not do.
- Current- and future-state workflow map
- One use case, chosen for value
- Code, agent or human, decided for every step
- Expected value in hours, cost and errors
- The approval screen, prototyped in code
Fixed price · 30 days
Most teams start here30-Day Deployment
Sprint, build, evals and a shadow deployment on top of your existing systems, ending in a go/no-go.
- Everything in the Design Sprint
- A working agent over your data and APIs
- Guardrails, audit log and human approval
- An eval report: pass rate and failure categories
- Shadow deployment and a go/no-go readout
- 14 days of post-deployment support
Monthly · ongoing
Deployment Retainer
For systems already in production. Watch it, re-run the evals, improve it, and grant more autonomy only as it's earned.
- Cost, latency and failure review
- Evals re-run on every change
- New workflows audited and added
- Autonomy expanded one step at a time
Stack & delivery
Delivered as production code in your stack and your infrastructure, merged by pull request — never a slide deck.
Availability
One sprint left in September, then two in Q4. I run one sprint at a time, so the calendar is the real constraint — not a sales tactic.
Not sure which fits? Book the free review — twenty minutes will tell us both.
Alejandro Haydar
Forward Deployed Engineer · Founder, Alebuild
I've spent 10+ years shipping products across healthcare, fintech, and edtech — three acquired, and a run as Director of UX — and the last two building AI into them. Most teams don't need another model. They need someone to sit with the people doing the work, decide where AI belongs, and ship a system those people will actually adopt. I operate like a founder because I've built like one.
Deploying AI into real workflows, on top of the systems teams already run.
Not sure which fits? Book the free review — twenty minutes will tell us both.
Book your free workflow reviewSample a reviewPerplexity Sample ReviewLinkedInalejandrohaydarX@alebuildco